GRAPHICAL ABSTRACT

Highlights

• Breathing intervention increased vagal activity in healthy adults

• Breathing intervention did not influence glucose metabolism in healthy adults

• More insulin-resistant adults improved glucose metabolism via breathing intervention

INTRODUCTION

The global prevalence of diabetes has quadrupled from 1980 to 2014 and numbers are further increasing worldwide [1]. Diabetes is linked to cardiovascular disease, nephropathy, retinopathy, neuropathy, and further complications, with ultimately lead to higher mortality in those living with the disease [2].

The regulation of glucose metabolism is a complex process involving multiple organs that interact with each other [3]. While the autonomic nervous system (ANS) appears to play a significant role in this coordination, the extent of its contribution is still under investigation [4]. Nonetheless, increasing evidence indicates a critical connection between the ANS and glucose metabolism, suggesting that the ANS may have a crucial role in coordinating metabolic homeostasis [5]. Therefore, the brain communicates via the ANS with metabolically active organs to modulate insulin sensitivity and insulin secretion [3,6,7]. On the anatomical level, the ANS innervates key metabolic organs, including the liver and the pancreas to modulate glucose metabolism [8]. Of notice, brain derived modulation of both, insulin sensitivity and insulin secretion are thought to be impaired in people with type 2 diabetes mellitus [9]. In line, individuals with prediabetes and diabetes display an impaired balance between sympathetic and parasympathetic tone [10].

In humans, autonomic balance can be assessed through respiratory sinus arrhythmia, which measures natural variations in heart rate during the breathing cycle [11]. Respiratory sinus arrhythmia is regulated by vagal efferent pathways from the nucleus ambiguus of the brain [12]. Indeed, Heni et al. [13] found a positive correlation between changes in insulin sensitivity and the high-frequency (HF) band in the heart rate variability (HRV) spectrum, an index for respiratory sinus arrhythmia [14].

Additionally, the ANS affects glucose metabolism indirectly by regulating systemic inflammation through the cholinergic anti-inflammatory pathway. This pathway is primarily driven by stimulation of the dorsal motor nucleus of the vagus nerve, the main nerve of the parasympathetic nervous system [15]. There, efferent vagal neurons release acetylcholine, which binds to the α7 nicotinic receptors of cytokine-producing cells, including macrophages [16,17]. This anti-inflammatory pathway results in suppression of pro-inflammatory cytokine release and protection against systemic inflammation [18]. Impairments in the ANS thereby promote systemic subclinical inflammation, a pathophysiological contributor to whole-body insulin resistance [19].

There are still no specific therapeutic approaches to address the increased sympathetic tone in prediabetes and diabetes. Resonance frequency breathing (RFB) offers a potential method to stimulate the parasympathetic nervous system through slow-paced breathing frequencies. RFB maximizes HRV, a readout that mainly reflects the activity of the parasympathetic nervous system [11]. By rhythmizing breathing, heartbeat, and blood pressure, RFB enhances parasympathetic activity while suppressing sympathetic activity [20].

To facilitate RFB, this approach is often paired with HRV-biofeedback (BF), i.e., the measurement and visualization of unconscious body processes causing variation in time between heartbeats, allowing individuals to actively influence these processes [11]. Several studies have demonstrated that RFB-HRV-BF interventions can improve HRV, reduce stress and anxiety, and alleviate symptoms in patients with various medical conditions [21].

Hence, we now tested the potential of RFB-HRV-BF training to affect whole-body glucose metabolism. To do this, we assessed glucose metabolism before and after a 4-week RFA-HRV-BF intervention in healthy adults.

METHODS

Study participants

A power analysis (G*power version 3.1.9.2., Heinrich Heine University, Düsseldorf, Germany) indicated that a sample size of 27 participants would be required to detect a medium effect size of Cohen’s d=0.5 with a significance level of P=0.05 and a power of 80% (1–β=0.80), when using a dependent t-test to compare pre- and post-measurements. The study inclusion and exclusion criteria were checked in a standardized telephone interview using the Structured Clinical Interview for both axis I and axis II [22] according to the Diagnostic and Statistical Manual of Mental Disorders [23]. Exclusion criteria included any acute and/or chronic medical illness, medication affecting glucose metabolism (e.g., steroids), heart rate, or central nervous system (e.g., β blockers), history of prediabetes, mental disorders, illicit drug abuse, stressful life-events within the previous 6 months, and a body mass index <18.5 or >27 kg/m². Because of age-related attenuation of slow-paced breathing effects on cardiovascular variability [24], only participants between 18 and 35 years of age were included. Thirty healthy young adults were enrolled (Fig. 1). A detailed description of the participants is given in Table 1.

Study design

The participants were instructed to perform RFB with HRV-BF at home via an ambulatory training system over the course of 4 weeks. They were further instructed that no fundamental change in lifestyle, including diet and physical activity, should be taking place during the intervention period. This was checked using the questionnaires ‘Physical Activity, Exercise, and Sport Questionnaire’ (BSA [25]) and ‘Food Frequency Questionnaire’ (FFQ [26]). The participants were to do RFB-HRV-BF training for at least 100 minutes per week. The assessment of glucose metabolism, autonomic activity and well-being took place before and after the 4-week-training period. A detailed description of the study design is given in Fig. 1. The study protocol was approved by the local Ethics Committee of the Landesärztekammer Rheinland-Pfalz, Germany (Nr. 2022-16489). All study procedures were conducted in accordance with the Principles of the Declaration of Helsinki for studies with human participants. All participants provided written informed consent prior to study initiation.

Resonance frequency breathing with HRV-BF

The mobile system ‘eSense Pulse’ by Mindfield Biosystems Ltd. (Gronau, Germany) was used for RFB-HRV-BF training. The participants sat in an upright position, started the training via the eSense app on their smartphone, and were instructed to practice breathing at their individually determined resonance frequency. The procedure of the mobile RFB-HRV-BF training is described in Herhaus et al. [27]. The data from each training session were saved in the individual and pseudonymized participant’s eSense account. The electrocardiography (ECG) recordings were analyzed with Kubios software (Kubios Oy, Kuopio, Finland).

Outcomes

Glucose metabolism and laboratory measurements

Glucose metabolism was assessed through a frequently-sampled 75 g oral glucose tolerance test (OGTT) starting at 8:00 AM after an overnight fast. Participants received detailed instructions on diet and exercise the day before the test to standardize conditions. An intravenous cannula was inserted 45 minutes before the first blood sample. After basal blood sampling, the participants drank a 75 g glucose solution, with further blood samples taken at 30, 60, 90, and 120 minutes after glucose ingestion. Blood samples were handled on ice, were immediately centrifuged, and stored at –80°C before batch measurements. Plasma glucose concentrations were determined by the hexokinase/glucose-6-phosphate dehydrogenase method, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides were quantified using a homogeneous enzymatic color assay and C-reactive protein concentrations were determined using a wide range turbidimetric immunoassay on the Alinity chemistry analyzer (Abbott, Wiesbaden, Germany). Serum insulin and C-peptide concentrations were measured on the Atellica solution immunoassay analyzer (Siemens Healthineers, Eschborn, Germany). Glycosylated hemoglobin (HbA1c) was determined using a high-performance liquid chromatography using the Variant-II Hemoglobin Testing-System (Bio-Rad Laboratories GmbH, Feldkirchen, Germany). All of these measurements have been performed in the accredited diagnostic laboratories of the University Hospital Tübingen, Germany and the University Medical Center Mainz, Germany. Measurements of the biosamples were conducted in a blinded fashion in routine diagnostic laboratories.

Areas under the curve were calculated based on the trapezoid method. Glucose stimulated insulin secretion was assessed as oral disposition index and as ratio of the areas under the C-peptide curves and the areas under the glucose curves during the first 30 minutes of the OGTT (AUC C-peptid_{0-30 min}/AUC glucose_{0-30 min}) [28]. For the estimation of insulin sensitivity, homeostasis model assessment-insulin resistance (HOMA-IR) was used for the fasting state and Matsuda index [29] for the post-glucose load situation. Insulin clearance was estimated by the ratio of the areas under the C-peptide and insulin curves during the OGTT (AUC C-peptide_0-120/AUC insulin_0-120).

Heart rate variability measurements

For the assessment of the ANS activity before and after the training period of 4 weeks, the short-term measurement (5 minutes) of sitting in an upright position was carried out. In addition, the individual RFB was determined using the protocol by Lehrer et al. [30]. For this purpose, the participants performed various breathing frequencies (6.5, 6.0, 5.5, 5.0, and 4.5 breaths per minute) for 2 minutes each while sitting. The breathing frequency that maximized the HRV parameter root mean square successive differences (RMSSD) the most, was identified as the individual RFB. The HRV system HRVScanner^TM (BioSign, Ottenhofen, Germany) was used for the measurement of a single-channel ECG with a sample rate of 500/sec. All recorded ECGs were analyzed using the software Kubios Premium (Kubios Oy) with the automatic beat correction algorithm [31] and the smoothness priors method of trend component rejection [32]. The rate of corrected beats was <0.25%. HRV was quantified by the following metrics: RMSSD, standard deviation of all NN-intervals (SDNN), power in the low-frequency band 0.04–0.15 Hz (HRV-LF), and power in the high-frequency range 0.15–0.4 Hz (HRV-HF). The evaluator analyzing the ECG recordings was blinded and did not know the allocation of the recording into the pre- or post-measurement.

Mental health

The degree of stressful situations in one’s life during the previous month and perceived chronic stress were captured by the German version of the Perceived Stress Scale (PSS [33]) and the German short version of the Trier Inventory for Chronic Stress (TICS-9 [34]). The Brief Symptom Inventory 18 (BSI-18 [35]) measured physical and psychological complaints of the previous 7 days. The FFQ [26] was used to estimate the consumption of foods and beverages during the 4 weeks’ intervention. The amount of total activity and sport/exercise activity during the 4 weeks’ intervention was assessed by the BSA [25].

Statistical analysis

The software programs used to analyze and visualize the data were SPSS Statistics version 26 (IBM Co., Armonk, NY, USA) and Prism v.8.3.0 (GraphPad Software). For the specification of the glucose, insulin, C-peptide and cortisol response in the OGTT, the AUC was calculated. The effects of the RFB-HRV-BF intervention on whole-body glucose metabolism, lipids, inflammation, and hematological parameters, HRV parameters, and psychological measures were analyzed by a dependent t-test. The HRV values were transformed by logarithm naturalis+1 to obtain approximately normal distributions. In addition, regression was calculated to predict the influence of baseline Matsuda index and baseline fasting glucose level on changes in glycemia after the 4 weeks’ intervention.

RESULTS

Effects of RFB-HRV-BF training on autonomic nervous system tone

Over the 4 intervention weeks, the participants trained on average±standard deviation 97±11 minutes/week. There was a significant increase in the HRV parameters SDNN and RMSSD over the course of 4 weeks RFB-HRV-BF training (P≤0.001) (Fig. 2). Concerning the 5-minute resting sitting position, a significant increase in HRV parameters SDNN, RMSSD, power in the HRV-LF, and power in the high-frequency range 0.15 to 0.4 Hz (HRV-HF) could be observed after the 4 weeks’ intervention (Fig. 3). There were no changes in the reported diet or the physical activity during the intervention period (FFQ: t(29)=1.649, P=0.11; BSA: t(29)=–1.464, P=0.15).

Effects of RFB-HRV-BF on glucose metabolism

After the 4 weeks training intervention, there were neither significant changes in fasting glucose, nor in 2-hour-post-load glucose (all P≥0.71) (Table 2, Fig. 4). In line, there were no significant changes in HbA1c (t(29)=1.667, P=0.10). Furthermore, insulin sensitivity (HOMA-IR P=0.52, Matsuda index P=0.28) and insulin secretion adjusted for insulin sensitivity (F_{(1, 52)}=1.245, P=0.27) remained unchanged.

Baseline insulin sensitivity (assessed through the Matsuda index) and baseline fasting glucose were significant predictors of changes in glycemia after the 4 weeks’ intervention (P≤0.05). Based on these results, subgroups of baseline Matsuda index and baseline fasting glucose were formed. Exploratory analyses revealed a significantly stronger reduction in glycemia during the intervention in the more insulin resistant half of the study participants (for fasting glucose [t(29)=–2.421, P≤0.05, d=–0.88]; for 2-hour-post-load glucose [t(29)=–2.303, P≤0.05, d=–0.84]; and for AUC glucose [t(29)=–2.401, P≤0.05, d=–0.88]) (Supplementary Table 1, Supplementary Fig. 1). There were also significant interaction effects in two-way analysis of variance (ANOVA) testing time (pre- and post-measurement)×group (low/high baseline Matsuda index) interactions on fasting glucose (F_{(1, 28)}=5.862, P≤0.05, η²=0.173), on 2-hiur-postload glucose (F_{(1, 28)}=5.304, P≤0.05, η²=0.159), and on AUC glucose (F_{(1, 28)}=5.764, P≤0.05, η²=0.171). Comparable results were obtained when comparing those with higher versus lower fasting glucose prior to intervention with significantly stronger reduction in glycemia during training in those with higher glucose (P≤0.001) (Supplementary Table 2, Supplementary Fig. 2). There were also significant interaction effects in two-way ANOVAs testing time (pre- and post-measurement)×group (low/high baseline fasting glucose) interactions on fasting glucose (F_{(1, 28)}=8.293, P≤0.01, η²=0.229), on 2-hour-postload glucose (F_{(1, 28)}=8.741, P≤0.01, η²=0.238), and on AUC glucose (F_{(1, 28)}=6.723, P≤0.05, η²=0.194).

Effect of RFB-HRV-BF on lipids and subclinical inflammation

During the intervention, there were no significant changes in lipids, cortisol, or C-reactive protein (all P≥0.05) (Table 2).

Effect of RFB-HRV-BF on mental health

The symptoms of somatization, depression, and anxiety demonstrated significantly lower total scores after the 4 weeks’ intervention (t(29)=2.586, P≤0.05, d=0.41). With regard to the perceived stress, there was no significant changes (P=0.22).

DISCUSSION

We here tested the effect of modulating ANS tone through RFB-HRV-BF on glucose metabolism in healthy adults. Our intervention was successful in modulating the ANS, but had no significant impact on metabolism when analyzing the entire cohort. However, the more insulin resistant half of our participants experienced improvements in glucose metabolism in response to the intervention. In addition, the training improved somatic and depressive symptoms but not perceived stress without differences between more and less insulin resistant persons.

Our findings are well in line with previous studies that demonstrated the potential of HRV-BF interventions in improving HRV and alleviate symptoms in patients with various medical conditions [21]. Interestingly, RFB-HRV-BF not only strengthens the modulation of the ANS and reduces the symptom severity of diseases, but there is also initial evidence of positive effects on immunological [36,37] and neurobiological processes [38]. A cross-over study did not find acute effects of short-term slow-paced breathing on glycemia in healthy men but reported potential links between heart rate and insulin secretion during the intervention [39].

The lack of effects on glucose metabolism after our current 4-week-intervention of RFB-HRV-BF despite an increase in vagal activity might be due to the fact that we included healthy persons who already had excellent glucose control and might therefore not be improvable. The most interesting finding of our current work is the possible impact of baseline glycemia and insulin sensitivity on the glycemic effects of our intervention. Those with a slightly higher glucose and those who were more insulin resistant showed improvement of glucose metabolism after the 4 weeks’ intervention. This suggests the possibility that RFB-HRV-BF intervention can enhance glucose metabolism in individuals with impaired glucose regulation. This effect likely involves both, the parasympathetic activation via maximized respiratory sinus arrhythmia [11] and the stimulation of the vagal nerve with its anti-inflammatory pathway [15] with potential impact on both insulin secretion and insulin sensitivity [40]. In addition to these potential benefits, RFB-HRV-BF might lead to additional improvements in cardiovascular risk in people with prediabetes or diabetes [10].

Several studies have also demonstrated positive effects on mental health through RFB-HRV-BF [21,41]. The afferent vagus nerves project to brain areas which mediate mood and emotional regulation [42]. Our data provide further support for a possible effect by means of lower somatization, depression, and anxiety symptoms after 4 weeks of intervention.

The strengths of this study are the determination of the individual RFB, the high ECG data quality with a low rate of corrected beats (<0.25%), the use of a standardized assessment of glucose metabolism through frequently-sampled OGTTs, and control of the confounders diet and physical activity during the intervention period. Although the RFB-HRV-BF system used in this study is proprietary, similar positive effects have been demonstrated with slow-paced breathing techniques [37,43], which can be performed using freely accessible mobile applications. This highlights the potential for broader adoption and accessibility of the intervention beyond the proprietary system used in the current study. However, there are limitations of the current study. Our study only included healthy volunteers. The sample size was furthermore too small and the intervention duration was too short to explore potential underlying mechanisms (e.g., impact of systemic inflammation) and to observe long-term metabolic changes (e.g., HbA1c). It must be considered that use of a median split has some limitations [44]. Although the study controlled lifestyle changes through self-reported questionnaires, objective measures, such as wearable activity trackers or diet logs, would enhance reliability. This study hypothesizes potential mechanisms, such as vagal activation and anti-inflammatory effects, but does not include direct measurements (e.g., cytokines or vagal nerve activity), which limits the ability to establish causal relationships.

In conclusion, our RFB-HRV-BF intervention was able to increase vagal activity in healthy individuals and may improve glucose metabolism in those who are more insulin resistant before the intervention. As brain-periphery communication through the ANS is crucial in modulating both insulin secretion and insulin sensitivity, further mechanistic studies are needed to clarify the precise mechanisms by which RFB-HRV-BF affects metabolism. Given the known increased sympathetic tone in prediabetes and diabetes, RFB-HRV-BF training may be a non-pharmacological option to restore ANS tone, with potential benefits for glucose metabolism. Future studies specifically targeting individuals with prediabetes or diabetes are needed to explore its potential benefits for improving impaired glucose metabolism.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

Outside of the current work, Martin Heni reports lecture fees from Chiesi/Amryt, AstraZeneca, Bayer, Boehringer Ingelheim, Lilly, Novartis, Novo Nordisk and Sanofi. He also served on advisory boards for Chiesi/Amryt, Boehringer Ingelheim, and Sanofi. Benedict Herhaus, Andreas Peter, Julia Hummel, Thomas Kubiak, and Katja Petrowski report no conflicts of interest.

AUTHOR CONTRIBUTIONS

Conception or design: B.H., J.H., M.H., K.P.

Acquisition, analysis, or interpretation of data: B.H., A.P., J.H., M.H., K.P.

Drafting the work or revising: all authors.

Final approval of the manuscript: all authors.

FUNDING

This study was funded by the Internal University Research Funding of the University Medical Center of the Johannes Gutenberg University Mainz & by the DFG-project ‘Effects of autonomic nervous system modulation by HRV-BF training with resonant frequency breathing on glucose metabolism in individuals with prediabetes’ (Project-number: 540546352).

ACKNOWLEDGMENTS

We thank the medical doctoral students Cécile Gobin, Caroline Rech, Sarah Rüther, and Josephine Spiegel (all University of Mainz) for assisting with data collection.

Fig. 1.

Study outline. (A) Consolidated Standards of Reporting Trials (CONSORT) flow diagram. (B) Study design of resonance frequency breathing with heart rate variability biofeedback intervention.

Fig. 2.

Changes in autonomic function showing in heart rate variability-parameters standard deviation of all NN-intervals (SDNN) and root mean square successive differences (RMSSD) during baseline and 4 weeks of intervention with resonance frequency breathing with heart rate variability biofeedback in healthy adults (n=30). Data presented as mean±standard deviation. ln, logarithm naturalis+1; ms, millisecond; RSB, resting sitting position with spontaneous breathing; W1, intervention week 1; W2, intervention week 2; W3, intervention week 3; W4, intervention week 4. ^aP≤0.001, significant difference between resting sitting position with spontaneous breathing and intervention week.

Fig. 3.

Box-and-Whisker plots of heart rate and heart rate variability (HRV)-parameters before and after 4 weeks intervention of resonance frequency breathing with heart rate variability biofeedback in healthy adults (n=30). Data presented as mean±standard deviation. ln, logarithm naturalis+1; ms, millisecond; SDNN, standard deviation of all NN-intervals; RMSSD, root mean square successive differences; LF, power in low-frequency range 0.04–0.15 Hz; HF, power in high-frequency range 0.15–0.4 Hz. ^aP≤0.05, significant difference between pre- and post-intervention value, ^bP≤0.01, significant difference between pre- and post-intervention value.

Fig. 4.

Changes in (A) blood glucose, (B) insulin, (C) C-peptide, and (D) cortisol after oral glucose tolerance test before and after 4 weeks of intervention with resonance frequency breathing with heart rate variability biofeedback in healthy adults (n=30). Data presented as mean±standard deviation.

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Herhaus B, Peter A, Hummel J, Kubiak T, Heni M, Petrowski K. Effect of 4 Weeks Resonance Frequency Breathing on Glucose Metabolism and Autonomic Tone in Healthy Adults. Diabetes Metab J. 2025 Apr 3. doi: 10.4093/dmj.2024.0647. Epub ahead of print.

Received: Oct 19, 2024; Accepted: Feb 03, 2025

DOI: https://doi.org/10.4093/dmj.2024.0647.

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